Best Tools for Visual Data Representation to Buy in November 2025

1

Storytelling with Data: A Data Visualization Guide for Business Professionals

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2

Data Visualization with Microsoft Power BI: How to Design Savvy Dashboards

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Good Charts Workbook: Tips, Tools, and Exercises for Making Better Data Visualizations

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4

Python Data Science Handbook: Essential Tools for Working with Data

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Business Intelligence Essentials You Always Wanted to Know: A Beginner’s Guide to BI Tools, Data Analytics Techniques, Data Visualization & Data-Driven Strategy (Self-Learning Management Series)

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Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures

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To show a progressive curve in matplotlib, you can create a line plot with increasing values on the y-axis as you move along the x-axis. This can be achieved by generating data points that follow a progressive pattern, such as a linear or exponential increase. Once you have your data points, you can plot them using the plt.plot() function in matplotlib. Additionally, you can customize the appearance of the curve by adjusting the color, line style, and other parameters in the plot function. By visualizing the progressive curve, you can effectively demonstrate a trend or growth pattern in your data.

What is the advantage of using subplots in matplotlib?

One advantage of using subplots in matplotlib is that it allows for the creation of multiple plots within a single figure. This can be used to compare or visualize different sets of data side by side, making it easier to see patterns or differences between them. Additionally, subplots can help in organizing and presenting complex data in a more structured and visually appealing manner. It also allows for more control over the layout and design of the plots within the figure.

How to display grid lines in a matplotlib plot?

You can display grid lines in a matplotlib plot using the grid() method. Here's how you can do it:

import matplotlib.pyplot as plt

Create some data

x = [1, 2, 3, 4, 5] y = [2, 4, 6, 8, 10]

Create the plot

plt.plot(x, y)

Display grid lines

plt.grid(True)

Show the plot

plt.show()

In this example, the grid() method is called with True as an argument to display grid lines on both the x and y axes. You can also customize the appearance of the grid lines by passing additional arguments to the grid() method, such as linestyle, color, and linewidth.

How to add a legend to a matplotlib plot?

To add a legend to a matplotlib plot, you can use the legend() function from the pyplot module. Here's a step-by-step guide on how to add a legend to a matplotlib plot:

1. Create your plot using matplotlib:

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5] y1 = [10, 15, 13, 18, 17] y2 = [5, 8, 7, 12, 11]

plt.plot(x, y1, label='Line 1') plt.plot(x, y2, label='Line 2')

plt.xlabel('x-axis') plt.ylabel('y-axis') plt.title('Plot Title')

1. Add a legend to the plot using the legend() function:

plt.legend()

1. Customize the legend by providing additional arguments to the legend() function. Some common arguments include loc to specify the location of the legend and title to set the legend title:

plt.legend(loc='upper right', title='Legend Title')

1. Finally, show the plot using the show() function:

plt.show()

By following these steps, you can easily add a legend to your matplotlib plot.

How to adjust the axis limits in a matplotlib plot?

You can adjust the limits of the x and y axes in a matplotlib plot using the set_xlim() and set_ylim() methods on the current Axes object.

Here's an example of how to adjust the x and y axis limits in a matplotlib plot:

import matplotlib.pyplot as plt

Create some data

x = [1, 2, 3, 4, 5] y = [10, 20, 15, 25, 30]

Create a plot

plt.plot(x, y)

Get the current Axes object

axes = plt.gca()

Set the x axis limits

axes.set_xlim(0, 6)

Set the y axis limits

axes.set_ylim(0, 35)

Show the plot

plt.show()

In this example, we first create a plot with some data points. We then get the current Axes object using plt.gca(), and use the set_xlim() method to set the x axis limits to be between 0 and 6, and the set_ylim() method to set the y axis limits to be between 0 and 35. Finally, we display the plot using plt.show().

How to display data points in a scatter plot using matplotlib?

To display data points in a scatter plot using matplotlib in Python, you can use the scatter() function from the pyplot module of matplotlib. Here is a step-by-step guide to creating a simple scatter plot with data points:

1. Import the necessary libraries:

import matplotlib.pyplot as plt

1. Create the data points (x, y) that you want to plot:

x = [1, 2, 3, 4, 5] y = [5, 4, 3, 2, 1]

1. Create a scatter plot by calling the scatter() function and passing in the x and y data points:

plt.scatter(x, y)

1. Add labels and title to the plot for better visualization:

plt.xlabel('X-axis Label') plt.ylabel('Y-axis Label') plt.title('Scatter Plot of Data Points')

1. Display the plot using plt.show():

plt.show()

This will create a scatter plot with the data points (x, y) displayed on the plot. You can customize the plot further by adding colors, sizes, and labels to the data points.Refer to the matplotlib documentation for more advanced customization options: https://matplotlib.org/stable/contents.html